
from torch import nn
import torch
# import torch.nn.functional as F
# import math
# https://zh-v2.d2l.ai/chapter_convolutional-modern/resnet.html#id4


#=====================================================================================================
#                           Client-side Model definition
#=====================================================================================================
# Model at client side
class BasicBlock(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes * self.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, planes * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * self.expansion)
            )

    def forward(self, x):
        out = torch.relu(self.bn1(self.conv1(x)))
        out = torch.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = torch.relu(out)
        return out

class ResNet50_client_side(nn.Module):
    def __init__(self):
        super(ResNet50_client_side, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(BasicBlock, 64, 3, stride=1)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = torch.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        return out

#=====================================================================================================
#                           Server-side Model definition
#=====================================================================================================
# Model at server side
class ResNet50_server_side(nn.Module):
    def __init__(self, num_classes):
        super(ResNet50_server_side, self).__init__()
        self.in_planes = 256  # 由于已经经过了self.layer1，所以更新in_planes

        self.layer2 = self._make_layer(BasicBlock, 128, 4, stride=2)
        self.layer3 = self._make_layer(BasicBlock, 256, 6, stride=2)
        self.layer4 = self._make_layer(BasicBlock, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.layer2(x)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out